Senior Manager – Analytics & AI

MUTHOOT PAPPACHAN TECHNOLOGIES LIMITED; Bengaluru; On-site; INR 4,000,000 - 6,400,000; Full time

Job summary

MUTHOOT PAPPACHAN TECHNOLOGIES LIMITED is seeking a Senior Manager – AI, Analytics & Data Engineering in Bengaluru to lead enterprise analytics, AI initiatives, reporting platforms, and data engineering for an NBFC portfolio. The role blends business strategy with advanced cloud analytics, machine learning, and stakeholder management to enable data-driven decisions across lending, risk, and operations.

The candidate should possess deep AWS analytics expertise, a track record of delivering AI/ML

Qualifications

• MBA/PGDM from a reputed institute and B.Tech/BE in related discipline.
• AWS Certifications preferred (Solutions Architect, Data Analytics, Machine Learning, AI Practitioner)
• Experience in analytics leadership and enterprise AWS analytics platforms is desirable
• Ability to translate business problems into analytical solutions and drive data-driven decisions
• Strong stakeholder management across CXOs and business units.

Responsibilities

• Lead enterprise-wide analytics initiatives supporting business growth.
• Build executive dashboards and management reporting.
• Develop analytical frameworks for lending, customer lifecycle, portfolio performance, collections, and profitability.
• Generate actionable business insights through descriptive, diagnostic, predictive, and prescriptive analytics.
• Present findings to senior leadership and business stakeholders.
• Design and manage enterprise data platforms using AWS technologies (Redshift, Glue, Lambda, Step Functions).
• Oversee AI/ML development including SageMaker, Bedrock, RAG, and vector databases.
• Ensure automated reporting, self-service analytics, and data governance across the organization.

Skills

Leadership

Strategic thinking

People management

Project management

Stakeholder communication

Business acumen

Education

MBA/PGDM

B.Tech/BE in CS/IT/Engineering/Statistics

Tools

AWS

Redshift

Glue

S3

Lambda

EC2

SageMaker

Bedrock

QuickSight

Power BI

SQL

Python

Excel

Job description

Job Title: Senior Manager – AI, Analytics & Data Engineering

Experience:12–18 years

Education

• MBA / PGDM from a reputed institute
• B.Tech / BE in Computer Science, Information Technology, Engineering, Statistics or related discipline
• AWS Certifications preferred (Solutions Architect, Data Analytics, Machine Learning, AI Practitioner)

About the Role

We are looking for an experienced analytics leader to drive enterprise analytics, AI initiatives, reporting platforms, and data engineering for a leading NBFC. The role combines business strategy, data engineering, machine learning, generative AI, cloud architecture, and stakeholder management to enable data-driven decision making across lending, collections, customer acquisition, risk, finance, and operations.

The ideal candidate should possess strong business acumen along with deep technical expertise in AWS analytics services, AI-assisted development, and modern reporting platforms. The Business understanding till Report delivery

Key Responsibilities

• Lead enterprise-wide analytics initiatives supporting business growth.
• Build executive dashboards and management reporting.
• Develop analytical frameworks for lending, customer lifecycle, portfolio performance, collections, and profitability.
• Generate actionable business insights through descriptive, diagnostic, predictive, and prescriptive analytics.
• Present findings to senior leadership and business stakeholders.

Design and manage enterprise data platforms using AWS technologies including:

• Amazon Redshift
• AWS Glue
• AWS Lambda
• AWS Step Functions

Technical Responsibilities include:

• Data warehouse architecture
• Performance optimisation
• Metadata management
• Cost optimisation

Artificial Intelligence & Machine Learning

• Amazon SageMaker
• Amazon Bedrock
• Retrieval Augmented Generation (RAG)
• Vector Databases
• Prompt Engineering
• Generative AI applications

Develop solutions such as:

• Lead scoring
• AI-powered reporting

AI-Assisted Software Development

Champion AI-powered engineering practices using tools such as:

• Cursor
• ChatGPT

Responsibilities include:

• AI-assisted coding
• Code review
• Workflow optimisation

Develop enterprise reporting using:

• Amazon QuickSight
• Microsoft Power BI
• SQL
• Executive MIS

Ensure:

• Automated reporting
• Self-service analytics
• Data governance

Business Analysis

• Gather and document business requirements.
• Translate business problems into analytical solutions.
• Conduct process improvement studies.
• Build business cases for AI and automation projects.
• Work closely with Product, Operations, Risk, Finance, Sales, and Technology teams.
• Lead and mentor analytics, BI, and data engineering teams.
• Define technical standards and best practices.
• Conduct architecture and code reviews.
• Drive innovation through AI adoption.
• Build analytics capability across the organisation.

Required Technical Skills

• AWS
• Redshift
• Glue
• S3
• Lambda
• EC2

AI & ML

• SageMaker
• Amazon Bedrock
• LLMs
• Prompt Engineering
• Python
• Scikit-learn

Programming

• SQL (Advanced)
• Python

Reporting

• Amazon QuickSight
• Power BI
• Excel (Advanced)

Databases

• SQL Server
• Redshift

Business Knowledge

Experience in one or more of the following:

• Gold Loans
• Personal Loans
• Loan Origination
• Collections
• Customer Analytics
• Branch Performance
• Cross Sell Analytics
• Regulatory Reporting

NBFC or Banking domain experience is preferred.

• Strategic thinking
• People leadership
• Problem solving
• Project management

Preferred Experience

• 12–18 years in analytics, BI, or data engineering.
• Experience managing enterprise AWS analytics platforms.
• Proven track record delivering AI and ML solutions in production.
• Experience working with CXOs and business leadership.
• Experience implementing GenAI solutions within financial services.

Success Measures

• Improved business decision-making through analytics.
• Reduced reporting turnaround time.
• Increased automation of reporting and data pipelines.
• Successful deployment of AI/ML solutions into production.
• High adoption of self-service analytics.
• Optimised cloud cost and platform performance.
• Strong stakeholder satisfaction and business impact.